ABSTRACT Older Americans experience approximately 29 million falls and 13 million hospitalizations per year. These intervening health events (IHE - episodic falls, injuries, illnesses, and hospitalizations) are strong precipitants of disability in older adults. Because of their episodic nature, IHEs are extremely difficult to study. Continuous, long-term monitoring with remote capabilities using wearable technology is an ideal solution for capturing information surrounding an IHE and in particular, preceding it. This R21/R33 project aims to develop a sustainable research infrastructure built on the foundation of a smart watch application and server called ROAMM (Real-time Online Assessment and Mobility Monitor). It will offer long-term and continuous connectivity, bidirectional interactivity and remote programming. ROAMM will create a detailed narrative about mobility (activity patterns, walking speed, life space), patient reported outcomes/symptoms (pain, poor mood, fatigue, disability), cognition (working memory, processing speed, and executive functioning) and reports of health events (falls and hospitalizations). The infrastructure is composed of a diverse group of investigators with expertise in mobile technology/data science and applied/medical sciences who will serve in the following cores: Wearable Technology, Phenotyping, Clinical Outcomes, Data Science Management & Quality, and Recruitment, Retention & Compliance. In the R21 phase, we will create the ROAMM framework consisting of the watch application and accompanying server. We will also assess test-retest reliability, convergent validity and participant usability/acceptability. Each year, an Independent Advisory Panel and External Advisory Committee will evaluate milestone-driving activities and our Go/No-Go checkpoints for transitioning to the R33 phase. Work proposed in the R33 phase will showcase the ROAMM infrastructure by conducting a prospective, longitudinal study (range 1.25-2.5 yrs) in 200 community-dwelling persons aged 70+ yrs. This phase will test a field deployable version of ROAMM in real world settings to address the following hypotheses: 1) Pre-event patterns of low mobility, disability, fatigue, pain and depressive mood collected by ROAMM are independent predictors of incident IHE's; 2) IHE's will negatively impact the course of ROAMM measures; and 3) Additional value will be gained for explaining the change variability and recovery trajectories. An exploratory aim will evaluate safety while using ROAMM features and identify predictors of ROAMM adherence using both key-informant interviews and examine demographic and health histories to create boundaries for using ROAMM and other systems like it for long-term, continuous monitoring in research and practice. We will sustain ROAMM by targeting grant opportunities for the wearable technology surge for remote patient interaction, adopting licensing fees, and aligning our services with larger entities to become the go-to place for remote data capture. These activities will create a sustainable infrastructure to ensure research on older adults is keeping pace with the state-of-the-art ?smart and connected? health with wearable technology.